A Robust Optimization Approach to Supply Chain Cost Optimization

Abstract

Exhibit 11: Sensitivity of the robust model to variations in cost of shipment from warehouses to markets. Conclusion: The objective of the robust model is to minimize the supply chain costs. The model is designed to incorporate the penalty costs in case demands of a certain market is not satisfied. A generated numerical example of supply chain network is provided. The objective function is solved following the robust optimization methods discussed in the literature and the optimal solutions are obtained when the decision constraints are satisfied. The optimal solutions for different conservatism degrees and variabilities in uncertain parameters are determined using the robust optimization model. It is shown how sensitive is the objective function to the changes in conservatism degrees. Changes in the optimal solutions are determined for a range of variabilities in the primary input parameters. It is found that the greatest conservatism degrees of demand and supply parameters result in the worst objective in terms of the supply chain costs. For location and shipment costs, the worst objectives are the results of conservatism degrees smaller than the maximum degrees. The results reveal that variation in demand causes the greatest changes in the optimal value of the objective function. The second most significant parameter is supply capacity.